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Automatic table detection and classification in large-scale newspaper archives
Related publications (44)
SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT
Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...
New York2023Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...
EPFL2023Text Representation Learning for Low Cost Natural Language Understanding
Jan Frederik Jonas Florian Mai
Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...
EPFL2023Why is the winner the best?
Jian Wang, Gabriel Girard, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...
Los Alamitos2023Where Did the News Come From? Detection of News Agency Releases in Historical Newspapers
Since their beginnings in the 1830s and 1840s, news agencies have played an important role in the national and international news market, aiming to deliver news as fast and as reliable as possible. While we know that newspapers have been using agency conte ...
2023Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning
Barbara Bruno, Jauwairia Nasir
In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...
2022Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations
Vasiliki Tileli, Reinis Ignatans
In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switch ...
WILEY-V C H VERLAG GMBH2022Robustness and invariance properties of image classifiers
Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...
EPFL2022Landscape and training regimes in deep learning
Matthieu Wyart, Mario Geiger, Leonardo Petrini
Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...
2021Loss landscape and symmetries in Neural Networks
Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...
EPFL2021